Genetic Programming and Evolvable Machines

, Volume 11, Issue 3–4, pp 339–363 | Cite as

Open issues in genetic programming

  • Michael O’Neill
  • Leonardo Vanneschi
  • Steven Gustafson
  • Wolfgang Banzhaf
Contributed Article

Abstract

It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP. We hope this overview will stimulate debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful problem solving algorithms.

Keywords

Open issues Genetic programming 

Notes

Acknowledgments

The impetus for this article arose out of the EuroGP 2008 debate on Grand Challenges of Genetic Programming which took place on 27 March 2008 at the Evo* event in Naples, Italy. In particular we thank the two other panel members, Nic McPhee and Riccardo Poli, and also the many members of the audience who participated in the debate. Many of these issues have been raised on multiple occasions at previous (and subsequent) EuroGP debates so this inspired us to put these ideas on paper to open the debate to a wider audience. MO’N acknowledges support of Science Foundation Ireland under Grant No. 08/IN.1/I1868. WB acknowledges support from the Canadian National Science and Engineering Research Council (NSERC) under discovery grant RGPIN 283304-07.

References

  1. 1.
    L. Altenberg, NK fitness landscapes. In Section B2.7.2 in Handbook of Evolutionary Computation, ed. by T. Back et al. (IOP Publishing Ltd and Oxford University Press, Bristol and Oxford, 1997), pp. B2.7:5–B2.7:10Google Scholar
  2. 2.
    L. Altenberg, Modularity in evolution: Some low-level questions. In Modularity: Understanding the Development and Evolution of Complex Natural Systems, ed. by D. Rasskin-Gutman, W. Callebaut (MIT Press, Cambridge, MA, 2004, in press)Google Scholar
  3. 3.
    P.J. Angeline, Two self-adaptive crossover operators for genetic programming. In Advances in Genetic Programming 2, ch. 5, ed. by P.J. Angeline, K.E. Kinnear, Jr. (MIT Press, Cambridge, MA, 1996), pp. 89–110Google Scholar
  4. 4.
    F. Archetti, S. Lanzeni, E. Messina, L. Vanneschi, Genetic programming for computational pharmacokinetics in drug discovery and development. Gene. Program. Evolvable Mach. 8(4), 413–432 (2007, Dec). Special issue on medical applications of Genetic and Evolutionary ComputationGoogle Scholar
  5. 5.
    A. Asuncion, D. Newman, UCI Machine Learning Repository (2007)Google Scholar
  6. 6.
    W. Banzhaf, Editorial introduction to the first issue. Genet. Program. Evolvable Mach. 1, 5–6 (2000)CrossRefGoogle Scholar
  7. 7.
    W. Banzhaf, G. Beslon, S. Christensen, J. Foster, F. Képès, V. Lefort, J. Miller, M. Radman, J. Ramsden, From artificial evolution to computational evolution: a research agenda. Nat. Rev. Genet. 7(9), 729–735 (2006)CrossRefGoogle Scholar
  8. 8.
    W. Banzhaf, F.D. Francone, P. Nordin, The effect of extensive use of the mutation operator on generalization in genetic programming using sparse data sets. In 4th International Conference on Parallel Problem Solving from Nature (PPSN96), ed. by W. Ebeling et al. (Springer, Berlin, 1996), pp. 300–309Google Scholar
  9. 9.
    W. Banzhaf, P. Nordin, R. E. Keller, F. D. Francone, Genetic Programming—An Introduction; On the Automatic Evolution of Computer Programs and its Applications (Morgan Kaufmann, San Francisco, CA, 1998)Google Scholar
  10. 10.
    W. Banzhaf, R. Poli, M. Schoenauer, T. Fogarty (eds.), Proceedings of Genetic Programming, 1st European Workshop, EuroGP’98, Paris, France, April 14–15, 1998, vol. 1391 of LNCS (Springer, Berlin, 1998)Google Scholar
  11. 11.
    L. Beadle, C. Johnson, Semantically driven crossover in genetic programming. In Proceedings of the IEEE World Congress on Computational Intelligence (Hong Kong, 1–6 June 2008), ed. by J. Wang, (IEEE Computational Intelligence Society, IEEE Press, 2008), pp. 111–116Google Scholar
  12. 12.
    S. Bhattacharyya, O. Pictet, G. Zumbach, Representational semantics for genetic programming based learning in high-frequency financial data. In Genetic Programming 1998: Proceedings of the 3rd Annual Conference (University of Wisconsin, Madison, WI, USA, 22–25 July 1998), ed. by J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M. H. Garzon, D.E. Goldberg, H. Iba, R. Riolo, (Morgan Kaufmann, 1998), pp. 11–16Google Scholar
  13. 13.
    S. Bianco, F. Gasparini, R. Schettini, L. Vanneschi, An evolutionary framework for colorimetric characterization of scanners. In International Workshop on Evolutionary Computation in Image Analysis and Signal Processing, EvoIASP 2008. Proceedings of Applications of Evolutionary Computing, EvoWorkshops 2008, vol. 4974/2008 of Lecture Notes in Computer Science, LNCS, ed. by M. Giacobini et al. (Springer, Berlin, Heidelberg, New York, 2008), pp. 245–254Google Scholar
  14. 14.
    M. Brameier, W. Banzhaf, Linear Genetic Programming. No. XVI in Genetic and Evolutionary Computation (Springer, Berlin, 2007)Google Scholar
  15. 15.
    J. Branke, Evolutionary Optimization in Dynamic Environments (Kluwer, Dordrecht, 2001)Google Scholar
  16. 16.
    E.K. Burke, M.R. Hyde, G. Kendall, Evolving bin packing heuristics with genetic programming. In Parallel Problem Solving from Nature—PPSN IX (Reykjavik, Iceland, 9–13 Sept 2006), vol. 4193 of LNCS, ed. by T.P. Runarsson, H.-G. Beyer, E. Burke, J.J. Merelo-Guervos, L.D. Whitley, X. Yao (Springer, 2006), pp. 860–869Google Scholar
  17. 17.
    R. Cleary, M. O’Neill, An attribute grammar decoder for the 01 multiconstrained knapsack problem. In Evolutionary Computation in Combinatorial Optimization—EvoCOP 2005 (Lausanne, Switzerland, 30 March–1 April 2005), vol. 3448 of LNCS, ed. by G.R. Raidl, J. Gottlieb, (Springer, 2005), pp. 34–45Google Scholar
  18. 18.
    N.L. Cramer, A representation for the adaptive generation of simple sequential programs. In Proceedings of the International Conference on Genetic Algorithms and Their Applications (Carnegie-Mellon University, Pittsburgh, PA, July 1985), ed. by J.J. Grefenstette, pp. 183–187Google Scholar
  19. 19.
    L.E. Da Costa, J.-A. Landry, Relaxed genetic programming. In GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (Seattle, WA, USA, 8–12 July 2006), vol. 1, ed. byn M. Keijzer et al. (ACM Press, 2006), pp. 937–938Google Scholar
  20. 20.
    J.M. Daida, R. Bertram, S. Stanhope, J. Khoo, S. Chaudhary, O. Chaudhary, What makes a problem GP-hard? Analysis of a tunably difficult problem in genetic programming. Genet. Program. Evolvable Mach. 2, 165–191 (2001)MATHCrossRefGoogle Scholar
  21. 21.
    J.M. Daida, H. Li, R. Tang, A.M. Hilss, What makes a problem GP-hard? Validating a hypothesis of structural causes. In Genetic and Evolutionary Computation—GECCO-2003, vol. 2724 of LNCS, ed. by E.C.-P. et. al. (Springer, Berlin, 2003), pp. 1665–1677Google Scholar
  22. 22.
    C. Darwin, On the Origins of the Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life (1859)Google Scholar
  23. 23.
    K. Deb, J. Horn, D. Goldberg, Multimodal deceptive functions. Complex Syst. 7, 131–153 (1993)MATHGoogle Scholar
  24. 24.
    I. Dempsey, M. O’Neill, A. Brabazon, Constant creation with grammatical evolution. Int. J. Innov. Comput. Appl. 1(1), 23–38 (2007)CrossRefGoogle Scholar
  25. 25.
    I. Dempsey, M. O’Neill, A. Brabazon, Foundations in Grammatical Evolution for Dynamic Environments, vol. 194 of Studies in Computational Intelligence (Springer, 2009, Apr)CrossRefGoogle Scholar
  26. 26.
    A.E. Eiben, M. Jelasity, A critical note on experimental research methodology in EC. In Congress on Evolutionary Computation (CEC’02) (Honolulu, HI, USA, 2002) (IEEE Press, Piscataway, NJ, 2002), pp. 582–587Google Scholar
  27. 27.
    A. Ekárt, S.Z. Németh, Maintaining the diversity of genetic programs. In Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002 (Kinsale, Ireland, 3–5 Apr 2002), vol. 2278 of LNCS, ed. by J.A. Foster, E. Lutton, J. Miller, C. Ryan, A.G.B. Tettamanzi (Springer, 2002), pp. 162–171Google Scholar
  28. 28.
    S.E. Eklund, Time series forecasting using massively parallel genetic programming. In Proceedings of Parallel and Distributed Processing International Symposium (22–26 Apr 2003), pp. 143–147Google Scholar
  29. 29.
    M. Evett, T. Fernandez, Numeric mutation improves the discovery of numeric constants in genetic programming. In Genetic Programming 1998: Proceedings of the 3rd Annual Conference (University of Wisconsin, Madison, WI, USA, 22–25 July 1998), ed. by J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, R. Riolo (Morgan Kaufmann, 1998), pp. 66–71Google Scholar
  30. 30.
    D. Fogel, Evolving computer programs. In Evolutionary Computation: The Fossil Record, ed. by D. Fogel (MIT Press, Cambridge, MA, 1998), ch. 5, pp. 143–144Google Scholar
  31. 31.
    L. Fogel, A. Owens, M. Walsh, Artificial Intelligence through Simulated Evolution (Wiley, New York, 1966)MATHGoogle Scholar
  32. 32.
    C. Fonlupt, Solving the ocean color problem using a genetic programming approach. Appl. Soft Comput. 1(1), 63–72 (2001, June)Google Scholar
  33. 33.
    F. Francone, The discipulus owner’s manual. URL: http://www.rmltech.com/technology_overview.htm (2004)
  34. 34.
    F.D. Francone, P. Nordin, W. Banzhaf, Benchmarking the generalization capabilities of a compiling genetic programming system using sparse data sets. In Genetic Programming: Proceedings of the 1st Annual Conference, ed. by J.R. Koza et al. (MIT Press, Cambridge, 1996), pp. 72–80Google Scholar
  35. 35.
    R. Friedberg, A learning machine: Part 1. IBM J Res. Dev. 2(1), 2–13 (1958)CrossRefMathSciNetGoogle Scholar
  36. 36.
    R. Friedberg, B. Dunham, J. North, A learning machine: Part 2. IBM J. Res. Dev. 282–287 (1959)Google Scholar
  37. 37.
    C. Gagne, Open beagle. URL: http://www.beagle.gel.ulaval.ca (11 2007)
  38. 38.
    C. Gagné, M. Schoenauer, M. Parizeau, Tomassini M., Genetic programming, validation sets, and parsimony pressure. In Genetic Programming, 9th European Conference, EuroGP2006, Lecture Notes in Computer Science, LNCS 3905, ed. by P. Collet et al. (Springer, Berlin, Heidelberg, New York, 2006), pp. 109–120Google Scholar
  39. 39.
    D.E. Goldberg, U.-M. O’Reilly, Where does the good stuff go, and why? how contextual semantics influence program structure in simple genetic programming. In Proceedings of the 1st European Workshop on Genetic Programming (Paris, 14–15 Apr 1998), vol. 1391 of LNCS, ed. by W. Banzhaf, R. Poli, M. Schoenauer, T.C. Fogarty, (Springer, 1998), pp. 16–36Google Scholar
  40. 40.
    S. Gustafson, An Analysis of Diversity in Genetic Programming. PhD thesis, School of Computer Science and Information Technology, (University of Nottingham, Nottingham, England, 2004, Feb)Google Scholar
  41. 41.
    S. Gustafson, L. Vanneschi, Operator-based distance for genetic programming: Subtree crossover distance. In Genetic Programming, 8th European Conference, EuroGP2005, Lecture Notes in Computer Science, LNCS 3447, ed. by M. Keijzer, et al. (Springer, Berlin, Heidelberg, New York, 2005), pp. 178–189Google Scholar
  42. 42.
    S. Gustafson, L. Vanneschi, Operator-based tree distance in genetic programming. IEEE Trans. Evol. Comput. 12, 4 (2008)Google Scholar
  43. 43.
    J. Hansen, P. Lowry, R. Meservy, D. McDonald, Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection. Decis. Support Syst. 43(4), 1362–1374CrossRefGoogle Scholar
  44. 44.
    E. Hemberg, C. Gilligan, M. O’Neill, A. Brabazon, A grammatical genetic programming approach to modularity in genetic algorithms. In Proceedings of the 10th European Conference on Genetic Programming (Valencia, Spain, 11–13 Apr 2007), vol. 4445 of Lecture Notes in Computer Science, ed. by M. Ebner, M. O’Neill, A. Ekárt, L. Vanneschi, A.I. Esparcia-Alcázar (Springer, 2007), pp. 1–11Google Scholar
  45. 45.
    G. Hornby (2006) ALPS: the age-layered population structure for reducing the problem of premature convergence. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, (ACM, New York, NY, USA, 2006), pp. 815–822Google Scholar
  46. 46.
    J. Hu, E. Goodman, K. Seo, Z. Fan, R. Rosenberg, The hierarchical fair competition (hfc) framework for sustainable evolutionary algorithms. Evol. Comput. 13(2), 241–277 (2005)CrossRefGoogle Scholar
  47. 47.
    T. Hu, W. Banzhaf, Neutrality and variability: two sides of evolvability in linear genetic programming. In GECCO ’09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (Montreal, 8–12 July 2009), ed. by G. Raidl, F. Rothlauf, G. Squillero, R. Drechsler, T. Stuetzle, M. Birattari, C. B. Congdon, M. Middendorf, C. Blum, C. Cotta, P. Bosman, J. Grahl, J. Knowles, D. Corne, H.-G. Beyer, K. Stanley, J.F. Miller, J. van Hemert, T. Lenaerts, M. Ebner, J. Bacardit, M. O’Neill, M. Di Penta, B. Doerr, T. Jansen, R. Poli, E. Alba, (ACM, 2009), pp. 963–970Google Scholar
  48. 48.
    T. Hu, W. Banzhaf, The role of population size in rate of evolution in genetic programming. In Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009 (Tuebingen, Apr 15–17 2009), vol. 5481 of LNCS, ed. by L. Vanneschi, S. Gustafson, A. Moraglio, I. De Falco, M. Ebner (Springer, 2009), pp. 85–96Google Scholar
  49. 49.
    E. Jablonka, M. Lamb, Evolution in Four Dimensions: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life (MIT Press, Cambridge, 2005)Google Scholar
  50. 50.
    D. Jakobović, L. Budin, Dynamic scheduling with genetic programming. In Proceedings of the 9th European Conference on Genetic Programming (Budapest, Hungary, 10–12 Apr. 2006), vol. 3905 of Lecture Notes in Computer Science, ed. by P. Collet, M. Tomassini, M. Ebner, S. Gustafson, A. Ekárt (Springer, 2006), pp. 73–84Google Scholar
  51. 51.
    I. Jonyer, A. Himes, Improving modularity in genetic programming using graph-based data mining. In Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference (Melbourne Beach, FL, USA, May 11–13 2006), ed. by G.C.J. Sutcliffe, R.G. Goebel (American Association for Artificial Intelligence, 2006), pp. 556–561Google Scholar
  52. 52.
    W. Kantschik, W. Banzhaf, Linear-tree GP and its comparison with other GP structures. In Genetic Programming, Proceedings of EuroGP’2001 (Lake Como, Italy, 18–20 Apr. 2001), vol. 2038 of LNCS, ed. by J.F. Miller, M. Tomassini, P.L. Lanzi, C. Ryan, A.G.B. Tettamanzi, W.B. Langdon (Springer, 2001), pp. 302–312Google Scholar
  53. 53.
    W. Kantschik, W. Banzhaf, Linear-graph GP—a new GP structure. In Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002 (Kinsale, Ireland, 3–5 Apr. 2002), vol. 2278 of LNCS, ed. by J.A. Foster, E. Lutton, J. Miller, C. Ryan, A.G.B. Tettamanzi (Springe, 2002), pp. 83–92Google Scholar
  54. 54.
    N. Kashtan, U. Alon, Spontaneous evolution of modularity and network motifs. In Proceedings of the National Academy of Sciences 102, 39 (27 Sept 2005), pp. 13773–13778Google Scholar
  55. 55.
    N. Kashtan, E. Noor, U. Alon, Varying environments can speed up evolution. In Proceedings of the National Academy of Sciences 104, 34 (21 Aug 2007), pp. 13711–13716Google Scholar
  56. 56.
    H. Katirai, Filtering junk E-mail: A performance comparison between genetic programming and naive bayes. 4A Year student project, 10 Sept 1999Google Scholar
  57. 57.
    M. Keijzer, V. Babovic, C. Ryan, M. O’Neill, M. Cattolico, Adaptive logic programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001) (San Francisco, California, USA, 7–11 July 2001), ed. by L. Spector, E.D. Goodman, A. Wu, W.B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M.H. Garzon, E. Burke (Morgan Kaufmann, 2001), pp. 42–49Google Scholar
  58. 58.
    R.E. Keller, R. Poli, Toward subheuristic search. In Proceedings of 2008 IEEE Congress on Evolutionary Computation (IEEE Press, 2008) pp. 3147–3154Google Scholar
  59. 59.
    K.E. Kinnear Jr., Fitness landscapes and difficulty in genetic programming. In Proceedings of the 1st IEEE Conference on Evolutionary Computing, (IEEE Press, Piscataway, NY, 1994), pp. 142–147Google Scholar
  60. 60.
    M. Kirschner, J. Gerhart, J. Norton, The plausibility of life: Resolving Darwin’s dilemma (Yale Univ Pr, 2006)Google Scholar
  61. 61.
    M. Kotanchek, The data modeler add-on package for mathematica. see http://www.evolved-analytics.com/datamodeler (72 2009)
  62. 62.
    J.R. Koza, Hierarchical genetic algorithms operating on populations of computer programs. In Proceedings of the 11th International Joint Conference on Artificial Intelligence IJCAI-89 (Detroit, MI, USA, 20–25 Aug 1989), vol. 1, ed. by N.S. Sridharan (Morgan Kaufmann, 1989), pp. 768–774Google Scholar
  63. 63.
    J.R. Koza, A genetic approach to the truck backer upper problem and the inter-twined spiral problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, vol. IV (IEEE Press, 1992), pp. 310–318Google Scholar
  64. 64.
    J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA, 1992)MATHGoogle Scholar
  65. 65.
    J.R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs (MIT Press, Cambridge MA, 1994)MATHGoogle Scholar
  66. 66.
    J.R. Koza, D. Andre, F.H. Bennett III, M. Keane, Genetic Programming 3: Darwinian Invention and Problem Solving (Morgan Kaufman, San Francisco, CA, 1999)Google Scholar
  67. 67.
    J.R. Koza, M.A. Keane, M.J. Streeter, W. Mydlowec, J. Yu, G. Lanza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence (Kluwer, Dordrecht, 2003)MATHGoogle Scholar
  68. 68.
    I. Kushchu, An evaluation of evolutionary generalization in genetic programming. Artif. Intell. Rev. 18(1), 3–14MATHCrossRefGoogle Scholar
  69. 69.
    W. Langdon, A many threaded cuda interpreter for genetic programming. In Proceedings of the 13th European Conference on Genetic Programming, vol. LNCS 6021, ed. by A.I. Esparcia-Alcázar, A. Ekárt, S. Silva, S. Dignum, A. Uyar (Springer, 2010), pp. 146–158Google Scholar
  70. 70.
    W. Langdon, W. Banzhaf, Repeated patterns in genetic programming. Nat. Comput. 7(4), 589–613 (2008)MATHCrossRefGoogle Scholar
  71. 71.
    W.B. Langdon, Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!, vol. 1 of Genetic Programming (Kluwer, Boston, 1998, Apr 24)Google Scholar
  72. 72.
    W.B. Langdon, W. Banzhaf, Genetic programming bloat without semantics. In Parallel Problem Solving from Nature—PPSN VI 6th International Conference (Paris, France, 16–20 Sept. 2000), vol. 1917 of LNCS, ed. by M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, H.-P. Schwefel (Springer, Berlin, 2000), pp. 201–210Google Scholar
  73. 73.
    W. B. Langdon, W. Banzhaf, Repeated sequences in linear genetic programming genomes. Complex Syst. 15(4), 285–306 (2005)MATHMathSciNetGoogle Scholar
  74. 74.
    W. B. Langdon, W. Banzhaf, Repeated patterns in genetic programming. Nat. Comput. 7(4), 589–613 (2008, Dec)MATHCrossRefGoogle Scholar
  75. 75.
    W.B. Langdon, S. Gustafson, J.R. Koza, GP Bibliography. http://www.cs.bham.ac.uk/wbl/biblio/gp-bib-info.html (2008)
  76. 76.
    W.B. Langdon, R. Poli, Genetic programming bloat with dynamic fitness. In Proceedings of the 1st European Workshop on Genetic Programming (Paris, 149-15 Apr 1998), vol. 1391 of LNCS, ed. by W. Banzhaf, R. Poli, M. Schoenauer, T. C. Fogarty (Springer, 1998), pp. 96–112Google Scholar
  77. 77.
    W.B. Langdon, R. Poli, Foundations of Genetic Programming (Springer, Berlin, 2002)MATHGoogle Scholar
  78. 78.
    W.-C. Lee, Genetic programming decision tree for bankruptcy prediction. In Proceedings of the 2006 Joint Conference on Information Sciences, JCIS 2006 (Kaohsiung, Taiwan, ROC, 8–11 Oct 2006) (Atlantis Press, 2006)Google Scholar
  79. 79.
  80. 80.
    T. McConaghy, H. Leung, V. Varadan, Functional reconstruction of dynamical systems from time series using genetic programming. In 26th Annual Conference of the IEEE Industrial Electronics Society, IECON 2000 (Nagoya, 22–28 Oct 2000), vol. 3, (IEEE, 2000), pp. 2031–2034Google Scholar
  81. 81.
    R.I.B. McKay, N.X. Hoai, P.A. Whigham, Y. Shan, M. O’Neill, Grammar-based genetic programming a survey. Genet. Program. Evolvable Mach. (this issue) (2010)Google Scholar
  82. 82.
    N.F. McPhee, B. Ohs, T. Hutchison, Semantic building blocks in genetic programming. In Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008 (Naples, 26–28 Mar. 2008), vol. 4971 of Lecture Notes in Computer Science, ed. by M. O’Neill, L. Vanneschi, S. Gustafson, A.I. Esparcia Alcazar, I. De Falco, A. Della Cioppa, E. Tarantino (Springer, 2008), pp. 134–145Google Scholar
  83. 83.
    J. Merelo, M. Keijzer, M. Schoenauer, Eo Evolutionary Computation Framework. URL: http://www.eodev.sourceforge.net/ (2006)
  84. 84.
    M. Mitchell, S. Forrest, J. Holland, The royal road for genetic algorithms: fitness landscapes and ga performance. In Toward a Practice of Autonomous Systems, Proceedings of the 1st European Conference on Artificial Life, ed. by F.J. Varela, P. Bourgine (The MIT Press, Cambridge, 1992), pp. 245–254Google Scholar
  85. 85.
    T. Mitchell, Machine Learning (McGraw Hill, New York, 1996)MATHGoogle Scholar
  86. 86.
    D.J. Montana, Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)CrossRefGoogle Scholar
  87. 87.
    J. Moore, P. Andrews, N. Barney, B. White, Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility to common human diseases. Lect. Notes Comput. Sci. 4973, 129–140 (2008)CrossRefGoogle Scholar
  88. 88.
    J. Moore, C. Greene, P. Andrews, B. White, Does Complexity Matter? Artificial Evolution, Computational Evolution and the Genetic Analysis of Epistasis in common human Diseases. Genet. Program. Theory Practice VI, 125 (2008)Google Scholar
  89. 89.
    R. Morrison, Designing Evolutionary Algorithms for Dynamic Environments (Springer, Berlin, 2004)MATHGoogle Scholar
  90. 90.
    Q.U. Nguyen, T.H. Nguyen, X.H. Nguyen, M. O’Neill, Improving the generalisation ability of genetic programming with semantic similarity based crossover. In vol. LNCS 6021, ed. by A.I. Esparcia-Alcázar, A. Ekárt, S. Silva, S. Dignum, A. Uyar (Springer), pp. 184–195Google Scholar
  91. 91.
    Q.U. Nguyen, M. O’Neill, X. H. Nguyen, B. McKay, E.G. Lopez, Semantic similarity based crossover in GP: The case for real-valued function regression. In Evolution Artificielle, 9th International Conference (26–28 Oct 2009), Lecture Notes in Computer Science, ed. by P. Collet, pp. 13–24Google Scholar
  92. 92.
    M. Nicolau, M. Schoenauer, W. Banzhaf, Evolving genes to balance a pole. In vol. LNCS 6021, ed. by A.I. Esparcia-Alcázar, A. Ekárt, S. Silva, S. Dignum, A. Uyar (Springer), pp. 196–207Google Scholar
  93. 93.
    P. Nordin, W. Banzhaf, F.D. Francone, introns in nature and in simulated structure evolution. In Bio-Computation and Emergent Computation (Skovde, Sweden, 1–2 Sept 1997), ed. by D. Lundh, B. Olsson, A. Narayanan (World Scientific Publishing, 1997)Google Scholar
  94. 94.
    M. Oltean, Evolving evolutionary algorithms using linear genetic programming. Evol. Comput. 13(3)(Fall 2005), 387–410CrossRefGoogle Scholar
  95. 95.
    M. O’Neill, A. Brabazon, Recent patents in genetic programming. Recent Pat. Comput. Sci. 2(1)(2009),43–49CrossRefGoogle Scholar
  96. 96.
    M. O’Neill, J. McDermott, J.M. Swafford, J. Byrne, E. Hemberg, E. Shotton, C. McNally, A. Brabazon, M. Hemberg, Evolutionary design using grammatical evolution and shape grammars: Designing a shelter. Int. J. Des. Eng. 3 (2010)Google Scholar
  97. 97.
    M. O’Neill, C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language, vol. 4 of Genetic Programming (Kluwer, 2003)Google Scholar
  98. 98.
    U.-M. O’Reilly, M. Hemberg, Integrating generative growth and evolutionary computation for form exploration. In Genetic Programming and Evolvable Machines 8, 2 (June 2007), pp. 163–186. Special issue on developmental systemsGoogle Scholar
  99. 99.
    A. Orfila, J.M. Estevez-Tapiador, A. Ribagorda, Evolving high-speed, easy-to-understand network intrusion detection rules with genetic programming. In Applications of Evolutionary Computing, EvoWorkshops2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoPhD, EvoSTOC, EvoTRANSLOG (Tubingen, Germany, 15–17 Apr 2009), ed. by M. Giacobini, I. De Falco, M. Ebner (LNCS, Springer, 2009)Google Scholar
  100. 100.
    P. Domingos. The role of Occam’s razor in knowledge discovery. Data Min Knowl Discov 3(4), 409–425 (1999)CrossRefGoogle Scholar
  101. 101.
    R. Poli, M. Graff, (2009) There is a free lunch for hyper-heuristics, genetic programming and computer scientists. In Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009 (Tuebingen, Apr 15–17 2009), vol. 5481 of LNCS, ed. by L. Vanneschi, S. Gustafson, A. Moraglio, I. De Falco, M. Ebner (Springer, 2009), pp. 195–207Google Scholar
  102. 102.
    R. Poli, M. Graff, N.F. McPhee, Free lunches for function and program induction. In FOGA ’09: Proceedings of the 10th ACM SIGEVO Workshop on Foundations of Genetic Algorithms (Orlando, FL, USA, 9–11 Jan 2009) (ACM, 2009), pp. 183–194Google Scholar
  103. 103.
    R. Poli, W.B. Langdon, O. Holland, Extending particle swarm optimisation via genetic programming. In Proceedings of the 8th European Conference on Genetic Programming (Lausanne, Switzerland, 30 Mar–1 Apr 2005), vol. 3447 of Lecture Notes in Computer Science, ed. by M. Keijzer, A. Tettamanzi, P. Collet, J.I. van Hemert, M. Tomassini (Springer, 2005), pp. 291–300Google Scholar
  104. 104.
    R. Poli, W.B. Langdon, N.F. McPhee, A Field Guide to Genetic Programming. Published via http://www.lulu.com and freely available at http://www.gp-field-guide.org.uk (2008). (With contributions by J.R. Koza)
  105. 105.
    R. Poli, N.F. McPhee, Exact schema theorems for GP with one-point and standard crossover operating on linear structures and their application to the study of the evolution of size. In Genetic Programming, Proceedings of EuroGP’2001, vol. 2038 of LNCS, ed. by J. Miller, M. Tomassini, P.L. Lanzi, C. Ryan, A. Tettamanzi, W. Langdon (Springer, 2001), pp. 126–142Google Scholar
  106. 106.
    R. Poli, N.F. McPhee, General schema theory for genetic programming with subtree swapping crossover: Part I. Evol. Comput. 11(1):53–66CrossRefGoogle Scholar
  107. 107.
    R. Poli, N.F. McPhee, General schema theory for genetic programming with subtree swapping crossover: Part II. Evol. Comput. 11(2):169–206CrossRefGoogle Scholar
  108. 108.
    R. Poli, L. Vanneschi, Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms. InGenetic and Evolutionary Computation Conference, GECCO’07, ed. by D. Thierens et al. (ACM Press, 2007), pp. 1335–1342Google Scholar
  109. 109.
    R. Poli, L. Vanneschi, W.B. Langdon, N.F. McPhee, Theoretical results in genetic programming: The next ten years?. Genet. Program. Evolvable Mach. (this issue) (2010)Google Scholar
  110. 110.
    B. Punch, D. Zongker, E. Goodman, (1996) The royal tree problem, a benchmark for single and multiple population genetic programming. In Advances in Genetic Programming 2, ed. by P. Angeline, K. Kinnear (The MIT Press, Cambridge, MA, 1996), pp. 299–316Google Scholar
  111. 111.
    J. Rissanen, Modeling by shortest data description. Automatica 14, 465–471 (1978)MATHCrossRefGoogle Scholar
  112. 112.
    J.P. Rosca, Towards automatic discovery of building blocks in genetic programming. In Working Notes for the AAAI Symposium on Genetic Programming (AAAI, 1995), pp. 78–85Google Scholar
  113. 113.
    F. Rothlauf, Representations for genetic and evolutionary algorithms, 2nd edn. (Springer, pub-SV:adr, 2006). First published 2002, 2nd edition available electronicallyGoogle Scholar
  114. 114.
    F. Rothlauf, M. Oetzel, On the locality of grammatical evolution. In Proceedings of the 9th European Conference on Genetic Programming (Budapest, Hungary, 10–12 Apr 2006), vol. 3905 of Lecture Notes in Computer Science, ed. by P. Collet, M. Tomassini, M. Ebner, S. Gustafson, A. Ekárt (Springer, 2006), pp. 320–330Google Scholar
  115. 115.
    C. Ryan, M. Keijzer, An analysis of diversity of constants of genetic programming. In Genetic Programming, Proceedings of EuroGP’2003 (Essex, 14–16 Apr 2003), vol. 2610 of LNCS, ed. by C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, E. Costa (Springer, 2003), pp. 404–413Google Scholar
  116. 116.
    G. Seront, External concepts reuse in genetic programming. In Working Notes for the AAAI Symposium on Genetic Programming (MIT, Cambridge, MA, USA, 10–12 Nov 1995), ed. by E.V. Siegel, J.R. Koza (AAAI, 1995), pp. 94–98Google Scholar
  117. 117.
    S. Shekhar, M.B. Amin, Generalization by neural networks. IEEE Trans. Knowl. Data Eng. 4 (1992)Google Scholar
  118. 118.
    S. Silva, L. Vanneschi, Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction. In GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (Montreal, 8–12 July 2009), ed. by G. Raidl, F. Rothlauf, G. Squillero, R. Drechsler, T. Stuetzle, M. Birattari, C. B. Congdon, M. Middendorf, C. Blum, C. Cotta, P. Bosman, J. Grahl, J. Knowles, D. Corne, H.-G. Beyer, K. Stanley, J.F. Miller, J. van Hemert, T. Lenaerts, M. Ebner, J. Bacardit, M. O’Neill, M. Di Penta, B. Doerr, T. Jansen, R. Poli, E. Alba (ACM, 2009), pp. 1115–1122Google Scholar
  119. 119.
    S.G.O. Silva, GPLab. A Genetic Programming Toolbox for MATLAB, 2008. See http://www.gplab.sourceforge.net
  120. 120.
    S. Smith, A learning system based on genetic adaptive algorithms Google Scholar
  121. 121.
    A.J. Smola, B. Scholkopf. A Tutorial on Support Vector Regression. Tech. Rep. Technical Report Series - NC2-TR-1998-030, NeuroCOLT2 (1999)Google Scholar
  122. 122.
    D. Song, M.I. Heywood, A.N. Zincir-Heywood, A linear genetic programming approach to intrusion detection. In Genetic and Evolutionary Computation—GECCO-2003 (Chicago, 12–16 July 2003), vol. 2724 of LNCS, ed. by E. Cantú-Paz, J. A. Foster, K. Deb, D. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M.A. Potter, A.C. Schultz, K. Dowsland, N. Jonoska, J. Miller (Springer, 2003), pp. 2325–2336Google Scholar
  123. 123.
    L. Spector, Evolving control structures with automatically defined macros. In Working Notes for the AAAI Symposium on Genetic Programming (MIT, Cambridge, MA, USA, 10–12 Nov 1995), ed. by E.V. Siegel, J.R. Koza (AAAI, 1995), pp. 99–105Google Scholar
  124. 124.
    L. Spector, A. Robinson, Genetic programming and autoconstructive evolution with the push programming language. Genet. Program. Evolvable Mach. 3(1), 7–40 (2002, March)Google Scholar
  125. 125.
    G.F. Spencer, Automatic generation of programs for crawling and walking. In Advances in Genetic Programming, ed. by K.E. Kinnear, Jr. (MIT Press, 1994), ch. 15, pp. 335–353Google Scholar
  126. 126.
    P.F. Stadler, Fitness landscapes. In Biological Evolution and Statistical Physics (Heidelberg, 2002), vol. 585 of Lecture Notes Physics, ed. by M. Lässig, Valleriani (Springer, 2002), pp. 187–207Google Scholar
  127. 127.
    P. Suganthan, N. Hansen, J. Liang, K. Deb, Y. Chen, A. Auger, S. Tiwari, Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Tech. Rep. Technical Report Number 2005005, Nanyang Technological University (2005)Google Scholar
  128. 128.
    A. Teller, M. Veloso, PADO: A new learning architecture for object recognition. In Symbolic Visual Learning, ed. by K. Ikeuchi, M. Veloso (Oxford University Press, Oxford, 1996), pp. 81–116Google Scholar
  129. 129.
    M. Tomassini, L. Vanneschi, P. Collard, M. Clergue, A study of fitness distance correlation as a difficulty measure in genetic programming. Evol. Comput. 13(2) (Summer 2005), 213–239CrossRefGoogle Scholar
  130. 130.
    L. Vanneschi, Theory and Practice for Efficient Genetic Programming. PhD thesis, Faculty of Sciences, University of Lausanne, Switzerland (2004)Google Scholar
  131. 131.
    L. Vanneschi, M. Castelli, S. Silva, Measuring bloat, overfitting and functional complexity in genetic programming. In GECCO ’10: Proceedings of the 12th Annual conference on Genetic and Evolutionary Computation, ed. by J. Branke (2010)Google Scholar
  132. 132.
    L. Vanneschi, G. Cuccu, Variable size population for dynamic optimization with genetic programming. In GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (Montreal, 8–12 July 2009), ed. by G. Raidl, F. Rothlauf, G. Squillero, R. Drechsler, T. Stuetzle, M. Birattari, C. B. Congdon, M. Middendorf, C. Blum, C. Cotta, P. Bosman, J. Grahl, J. Knowles, D. Corne, H.-G. Beyer, K. Stanley, J.F. Miller, J. van Hemert, T. Lenaerts, M. Ebner, J. Bacardit, M. O’Neill, M. Di Penta, B. Doerr, T. Jansen, R. Poli, E. Alba (ACM, 2009), pp. 1895–1896Google Scholar
  133. 133.
    L. Vanneschi, S. Gustafson, Using crossover based similarity measure to improve genetic programming generalization ability. In GECCO ’09: Proceedings of the 11th Annual conference on Genetic and Evolutionary Computation (New York, NY, USA, 2009) (ACM, 2009), pp. 1139–1146Google Scholar
  134. 134.
    L. Vanneschi, S. Gustafson, G. Mauri, Using subtree crossover distance to investigate genetic programming dynamics. In Genetic Programming, 9th European Conference, EuroGP2006, Lecture Notes in Computer Science, LNCS 3905, ed. by P. Collet et al. (Springer, Berlin, Heidelberg, New York, 2006), pp. 238–249Google Scholar
  135. 135.
    L. Vanneschi, D. Rochat, M. Tomassini, Multi-optimization improves genetic programming generalization ability. In GECCO ’07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (London, 7–11 July 2007), vol. 2, ed. by D. Thierens, H.-G. Beyer, J. Bongard, J. Branke, J. A. Clark, D. Cliff, C. B. Congdon, K. Deb, B. Doerr, T. Kovacs, S. Kumar, J.F. Miller, J. Moore, F. Neumann, M. Pelikan, R. Poli, K. Sastry, K. O. Stanley, T. Stutzle, R.A. Watson, I. Wegener (ACM Press, 2007), pp. 1759–1759Google Scholar
  136. 136.
    L. Vanneschi, M. Tomassini, P. Collard, S. Vérel, Negative slope coefficient. A measure to characterize genetic programming. In Proceedings of the 9th European Conference on Genetic Programming (Budapest, Hungary, 10–12 Apr. 2006), vol. 3905 of Lecture Notes in Computer Science, ed. by P. Collet, M. Tomassini, M. Ebner, S. Gustafson, A. Ekárt (Springer, 2006), pp. 178–189Google Scholar
  137. 137.
    E. J. Vladislavleva, G. F. Smits, D. den Hertog, Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. IEEE Trans. Evol. Comput. 13(2), 333–349 (2009, Apr)CrossRefGoogle Scholar
  138. 138.
    A. Wagner, Robustness and Evolvability in Living Systems (Princeton University Press, Princeton, NJ, 2005)Google Scholar
  139. 139.
    N. Wagner, Z. Michalewicz, M. Khouja, R. McGregor, Time series forecasting for dynamic environments: The dyfor genetic program model. IEEE Trans. Evol. Comput. 11(4), 433–452 (2006)CrossRefGoogle Scholar
  140. 140.
    D.C. Wedge, D.B. Kell, Rapid prediction of optimum population size in genetic programming using a novel genotype—fitness correlation. In GECCO ’08: Proceedings of the 10th annual conference on Genetic and evolutionary computation (Atlanta, GA, USA, 12–16 July 2008), ed. by M. Keijzer, G. Antoniol, C.B. Congdon, K. Deb, B. Doerr, N. Hansen, J. H. Holmes, G.S. Hornby, D. Howard, J. Kennedy, S. Kumar, F.G. Lobo, J.F. Miller, J. Moore, F. Neumann, M. Pelikan, J. Pollack, K. Sastry, K. Stanley, A. Stoica, E.-G. Talbi, I. Wegener (ACM, 2008), pp. 1315–1322Google Scholar
  141. 141.
    W. Weimer, T. Nguyen, C. Le Gues, S. Forrest, Automatically finding patches using Genetic Programming. In International Conference on Software Engineering (ICSE) 2009, (ACM, New York, NY, 2009) pp. 364–374Google Scholar
  142. 142.
    P.A. Whigham, Grammatical Bias for Evolutionary Learning. PhD thesis, School of Computer Science, University College, University of New South Wales, Australian Defence Force Academy, Canberra, Australia, 14 Oct 1996Google Scholar
  143. 143.
    P.A. Whigham, Grammatically-based genetic programming. In Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications (Tahoe City, CA, USA, 9 July 1995), ed. by J.P. Rosca, pp. 33–41Google Scholar
  144. 144.
    G. Wilson, M. Heywood, Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings. Genet. Program. Evolvable Mach. 8(2), 187–220 (2007, June) Special issue on developmental systemsGoogle Scholar
  145. 145.
    D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar
  146. 146.
    J.R. Woodward, Modularity in genetic programming. In Genetic Programming, Proceedings of EuroGP’2003 (Essex, 14–16 Apr 2003), vol. 2610 of LNCS, ed. by C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, E. Costa (Springer, 2003), pp. 254–263Google Scholar
  147. 147.
    S. Wright, The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In Proceedings of the 6th International Congress on Genetics, vol. 1, ed. by D. Jones (1932), pp. 355–366Google Scholar
  148. 148.
    H. Xie, M. Zhang, P. Andreae, Genetic programming for automatic stress detection in spoken english. In Applications of Evolutionary Computing, EvoWorkshops2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC (Budapest, 10–12 Apr 2006), vol. 3907 of LNCS, ed. by F. Rothlauf, J. Branke, S. Cagnoni, E. Costa, C. Cotta, R. Drechsler, E. Lutton, P. Machado, J.H. Moore, J. Romero, G.D. Smith, G. Squillero, H. Takagi (Springer, 2006), pp. 460–471Google Scholar
  149. 149.
    S. Yang, Y.-S. Ong, Y. Jin, Special issue on evolutionary computation in dynamic and uncertain environments. Genet. Program. Evolvable Mach. 7, 4 (2006)Google Scholar
  150. 150.
    M. Zhang, U. Bhowan, B. Ny, Genetic programming for object detection: A two-phase approach with an improved fitness function. Electron. Lett. Comput. Vis. Image Anal. 6(1), 27–43 (2006)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Michael O’Neill
    • 1
  • Leonardo Vanneschi
    • 2
  • Steven Gustafson
    • 3
  • Wolfgang Banzhaf
    • 4
  1. 1.Complex & Adaptive Systems Lab, School of Computer Science & InformaticsUniversity College DublinDublinIreland
  2. 2.University of Milano-BicoccaMilanItaly
  3. 3.GE Global ResearchNiskayunaUSA
  4. 4.Memorial University of NewfoundlandSt. John’sCanada

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